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USING BEHAVIOR MATRIX TO PERFORM CONTENT ADAPTATION IN MOBILE LEARNING

UoM Research Week

8 – 12 Nov 2021��AUTHORS:�CURUM Brita�KHEDO Kavi Kumar

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OVERVIEW

Introduction
Behavior Matrix or Personas
Users learning pattern or style
Implementation Structure of the Behavior Matrix in Mobile Learning
Two Scenarios are defined
The System Architecture
Results & Discussions
Future Works
Conclusion

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INTRODUCTION

The use of mobile devices whereby learning is supported while moving, leaving behind limitations of traditional educational environments

Mobile Learning

Location / Environment
Time
Behavior or Moods (Emotional state)
People / Identities
Objects

Context

Context-awareness

any information that can be used to characterize the situation of an entity (person, place or object)

Where you actually are ?

Who you are with ?

What resources are in proximity ?

So let’s start with mobile learning. Generally, mobile learning is defined as the use of mobile devices whereby learning is supported while moving, leaving behind limitations of traditional educational environments.

For mobile learning to happen, it is important to consider one key element which is the context. A context can be anything. It can be a location / environment, the time for example the time of the day, the behavior or moods of a person, people themselves and any objects.

Context-awareness is any information that can be used to characterize the situation of an entity (person, place or object). To define this 3 important questions are suggested:

Where you actually are ?... It can be our current location

Who you are with ?... Are we alone or accompanied or in a crowded environment?

What resources are in proximity ? For example, do we have any wifi connection etc.

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BEHAVIOR MATRIX OR PERSONAS

A behavior matrix or personas is a chart which features different attributes of a person’s attitude under distinct contexts��Personas are imaginary people representing different audiences such as learners, customers, marketers etc

Purpose: It is used to identify the right behavioral description of the person during learning

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USERS LEARNING PATTERN OR STYLE

Each individual learner presents a unique learning pattern or style

These are monitored using a pre-defined behavior matrix or personas for learning purposes

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IMPLEMENTATION STRUCTURE OF THE BEHAVIOR MATRIX IN MOBILE LEARNING

The Newbie Novice Neutral No important information retrieved from the learner’s profile	The Seasoned Artist Past experiences / skills Talented Information seeker	The Collaborator Collaborate learning Focused Going with the flow	The Specialist / Master / Professor Mastery Level Competent
The Goal Achiever Open to experience Willingness to learn new things Risk-taking Perseverance Focused New aspirations	The Observer Follow the trends Make small decisions Influenced with peers	The Lazybones Careless No planning Not focused No interests Wasting too much time Slow learner	The Explorer Adopting a growth mindset Learn new things Develop self-awareness Passion / purpose identifier New aspirations

This chart gives an overview of the proposed behavior matrix or persona to adapt contents in the framework

As we can see 8, distinct profiles are described:

�the newbie: Generally the best match for this profile would be a default one. If a person did not provide any information or very little information then this profile would be selected

�the seasoned artist: can be said to be the information seeker

�the collaborator: is the one who is focused and goes with the learning flow

the specialist: is the one who is at master level

�the goal-achiever: describes the profile which is open to new experience, is focused, shows perseverance and have new aspirations about the learning topic proposed

Next we have the observer: who follows the trends, shows some interests by making small decisions and is influenced with peers

the lazybones: the careless or lazy profile which shows no interest in the study

And finally we have the explorer: who adopts a growth mindset, looks forward to learning new things and have new aspirations.

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TWO SCENARIOS ARE DEFINED:

Scenario 1

No information about age, mood at the start of the application

Response = Neutral

Results: “The Newbie”

Scenario 2

Age, mood, previous knowledge of the person are confirmed, then

Response = A profile match

Results: “The Seasoned Artist”

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THE SYSTEM ARCHITECTURE

If we have a look at the system architecture, we can see that at the start of the mobile learning application, depending if the learner provided his personal information, and answers short quizzes or questions then they are provided with either the options of:

delivering the contextualized adapted courses or
showing the default courses.

This decision confirms the 1^st level of adaptation.

It should be noted that the quizzes or questions are prompted to the learner to know about their prior or past experiences. Questions that are asked are similar to:�

- In the range of 1 – 5, how familiar are you with this topic?

-

The prior knowledge assessment is important to know how the content adaptation will be presented to the learner; whether the course elements should be simple, basic, fundamental, progressive, advanced or complex.

Then we have a look at the 2^nd level of adaptation:

In this part, further questions are asked about the current mood or behavior of the learner. Here our focus is principally on the behavior of the person. Examples of questions asked are:

- How are the person feeling today? And the response can be energetic, calm, tense, angry, fearful etc…��At one end we receive the input from the learner and on the other side the reaction and behavior of the learner are monitored instantaneously through pre-defined Learning Record System using CMI5 object statements and are tracked in a Python algorithm. ��After having completed successfully all the above steps, we can adapt the learning contents based on the processed information and deliver same.

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FUTURE WORKS

1. Implementation of Bio sensors to track behavior of learner

Contribute to effective delivery of more personalized data based on the learner’s context information. �(Capturing the eye movement of a user could bring significant amendments while reading).

3. Investigate on pedagogical factors (human behavioral aspects and psychology)

2. Introduce a log system for user behavior model

A log system should be introduced to have a better insight of the user activities and adapt contents based on these actions.

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CONCLUSION

Students can set their own learning schedule based on their level of priority, their learning interests, their own pace, and also their current behavior

Customized / personalized learning for individual needs will increase the learning efficiency.

Mobile learning should continue its focus in constructing smart learning application using relevant context information at all levels.

Set goals and monitors own progress. Individuals are therefore more organized and are responsible for their actions.

Exhibits self-control and manages the emotions

In view of the evidence I have presented I think it is fair to say that:

To enhance today’s learning style, mobile learning should continue its focus in constructing smart learning application using relevant information at all levels to adapt contents for learning

Mobile learning allows customized / personalized learning for individual needs. This will increase the learning efficiency.

Students can set their own learning schedule based on their level of priority, their learning interests, their own pace, and also their current behavior

The learning patterns are customized for each individual, therefore goals are set and the learners can monitor their own progress. Also, the individuals are more organized and are responsible for their actions.

Students will be able to manage their emotions during learning and exhibit self-control.

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THANKS!

Any questions?

You can contact us at:�

brita_curum@hotmail.com

k.khedo@uom.ac.mu